SAILOR: Scaling Anchors via Insights into Latent Object Representation

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


LiDAR 3D object detection models are inevitably biased towards their training dataset. The detector clearly exhibits this bias when employed on a target dataset, particularly towards object sizes. However, object sizes vary heavily between domains due to, for instance, different labeling policies or geographical locations. State-of-the-art unsupervised domain adaptation approaches outsource methods to overcome the object size bias. Mainstream size adaptation approaches exploit target domain statistics, contradicting the original unsupervised assumption. Our novel unsupervised anchor calibration method addresses this limitation. Given a model trained on the source data, we estimate the optimal target anchors in a completely unsupervised manner. The main idea stems from an intuitive observation: by varying the anchor sizes for the target domain, we inevitably introduce noise or even remove valuable object cues. The latent object representation, perturbed by the anchor size, is closest to the learned source features only under the optimal target anchors. We leverage this observation for anchor size optimization. Our experimental results show that, without any retraining, we achieve competitive results even compared to state-of-the-art weakly-supervised size adaptation approaches. In addition, our anchor calibration can be combined with such existing methods, making them completely unsupervised.
Original languageEnglish
Title of host publicationProceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023
Number of pages10
ISBN (Electronic)9781665493468
Publication statusPublished - 2023
Event23rd IEEE/CVF Winter Conference on Applications of Computer Vision: WACV 2023 - Waikoloa, United States
Duration: 3 Jan 20237 Jan 2023

Publication series

NameProceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023


Conference23rd IEEE/CVF Winter Conference on Applications of Computer Vision
Abbreviated titleWACV 2023
Country/TerritoryUnited States
Internet address


  • Algorithms: 3D computer vision
  • and algorithms (including transfer, low-shot, semi-, self-, and un-supervised learning)
  • formulations
  • Image recognition and understanding (object detection, categorization, segmentation, scene modeling, visual reasoning)
  • Machine learning architectures

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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